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Record W1978292824 · doi:10.1063/1.1819999

Thermal-wave nondestructive evaluation of cylindrical composite structures using frequency-domain photothermal radiometry

2004· article· en· W1978292824 on OpenAlexaff
Chinhua Wang, Andreas Mandelis, Yue Liu

Bibliographic record

VenueJournal of Applied Physics · 2004
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceThermal conductivityNondestructive testingRadiometryCylinderThermalComposite numberThermal conductivity measurementThermal conductionInfraredOpticsComposite materialPhotothermal therapyField (mathematics)CoatingPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

In this paper, thermal-wave diagnostics by means of laser infrared photothermal radiometry (PTR) have been used for quantitative nondestructive evaluation of cylindrical composite structures. To quantitatively evaluate the thermal-wave field of a cylindrical composite material, the Green function corresponding to the composite structure and the PTR measurement scheme has been developed and subsequently the thermal-wave field has been derived. Furthermore, the characteristics of the thermal-wave field for two cases of practical interest, i.e., a cylindrical material with a surface coating and a cylindrical tube filled with a low thermal-conductivity fluid medium inside, are discussed. Experimental results from a stainless-steel (AISI 302) cylinder are used to validate the theoretical model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.251
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2004
Admission routes1
Has abstractyes

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